Stable Baselines Jax (SB3 + JAX = SBX)
See https://github.com/araffin/sbx
Proof of concept version of Stable-Baselines3 in Jax.
Implemented algorithms:
- Soft Actor-Critic (SAC) and SAC-N
- Truncated Quantile Critics (TQC)
- Dropout Q-Functions for Doubly Efficient Reinforcement Learning (DroQ)
- Proximal Policy Optimization (PPO)
- Deep Q Network (DQN)
- Twin Delayed DDPG (TD3)
- Deep Deterministic Policy Gradient (DDPG)
Example
from sbx import TQC, DroQ, SAC, DQN, PPO, TD3, DDPG
model = TQC("MlpPolicy", "Pendulum-v1", verbose=1)
model.learn(total_timesteps=10_000, progress_bar=True)
Release files for sbx-rl 0.8.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| sbx-rl-0.8.0.tar.gz | 29.3 kB | Without provenance | Details for sbx-rl-0.8.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| sbx_rl-0.8.0-py3-none-any.whl | Python 3 | none | any | Details for sbx_rl-0.8.0-py3-none-any.whl |
Total release size: 73.4 kB
Release files / sbx-rl-0.8.0.tar.gz
| Download URL | sbx-rl-0.8.0.tar.gz |
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| Size | 29.3 kB |
| Tags | Source |
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Release files / sbx_rl-0.8.0-py3-none-any.whl
| Download URL | sbx_rl-0.8.0-py3-none-any.whl |
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| Size | 44.1 kB |
| Tags | Python 3 |
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